Streamlining Operations for Servicing Field Locations:
The service technician’s responsibility is to manage customers effectively by ensuring their machines are never out of stock and product quality is good.
Leveraging Azure Machine Learning, we can forecast the anticipated demand across customers by taking in account drivers such as customer size, demographics, past preferences, weather and changing market conditions.
The Power App leverages these machine learning insights to provide the service technicians with answers to key questions like what products are needed to service today’s customers? What route should be taken? What to refill at each customer location?
With this customized mobile app, the service technician can restock in a quick and efficient manner.
The app provides interactive features, such as the ability to dynamically change routes or share notes with supervisors on any possible issues with the machines. Additional functionality and extensibility is easily and affordably developed as needed to meet each deployment’s unique requirements. The resulting business impacts include minimized stock outs, increased worker productivity, and increases in sales and asset profitability.
The solution leverages data from internal ERP, CRM, and/or IoT platforms and is paired with data from Neal Analytics partners like AccuWeather to get the most complete forecasting on anticipated demand.